{"title":"Neural decoding reveals dynamic patterns of visual chunk memory processes","authors":"Chantat Leong , Fei Gao , Zhen Yuan","doi":"10.1016/j.brainresbull.2025.111208","DOIUrl":null,"url":null,"abstract":"<div><div>Chunk memory constitutes the basic unit that manages long-term memory and converts it into immediate decision-making processes, it remains unclear how to interpret and organize incoming information to form effective chunk memory. This paper investigates electroencephalography (EEG) patterns from the perspective of time-domain feature extraction using chunk memory in visual statistical learning and combines time-resolved multivariate pattern analysis (MVPA). The GFP and MVPA results revealed that chunk memory processes occurred during specific time windows in the learning phase. These processes included attention modulation (P1), recognition and feature extraction (P2), and segmentation for long-term memory conversion (P6). In the decision-making stage, chunk memory processes were encoded by four ERP components. Scene processing correlated with P1, followed by feature extraction facilitated by P2, encoding process (P4), and segmentation process (P6). This paper identifies the early process of chunk memory through implicit learning and applies univariate and multivariate approaches to establish the neural activity patterns of the early chunk memory process, which provides ideas for subsequent related studies.</div></div>","PeriodicalId":9302,"journal":{"name":"Brain Research Bulletin","volume":"221 ","pages":"Article 111208"},"PeriodicalIF":3.5000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain Research Bulletin","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0361923025000206","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Chunk memory constitutes the basic unit that manages long-term memory and converts it into immediate decision-making processes, it remains unclear how to interpret and organize incoming information to form effective chunk memory. This paper investigates electroencephalography (EEG) patterns from the perspective of time-domain feature extraction using chunk memory in visual statistical learning and combines time-resolved multivariate pattern analysis (MVPA). The GFP and MVPA results revealed that chunk memory processes occurred during specific time windows in the learning phase. These processes included attention modulation (P1), recognition and feature extraction (P2), and segmentation for long-term memory conversion (P6). In the decision-making stage, chunk memory processes were encoded by four ERP components. Scene processing correlated with P1, followed by feature extraction facilitated by P2, encoding process (P4), and segmentation process (P6). This paper identifies the early process of chunk memory through implicit learning and applies univariate and multivariate approaches to establish the neural activity patterns of the early chunk memory process, which provides ideas for subsequent related studies.
期刊介绍:
The Brain Research Bulletin (BRB) aims to publish novel work that advances our knowledge of molecular and cellular mechanisms that underlie neural network properties associated with behavior, cognition and other brain functions during neurodevelopment and in the adult. Although clinical research is out of the Journal''s scope, the BRB also aims to publish translation research that provides insight into biological mechanisms and processes associated with neurodegeneration mechanisms, neurological diseases and neuropsychiatric disorders. The Journal is especially interested in research using novel methodologies, such as optogenetics, multielectrode array recordings and life imaging in wild-type and genetically-modified animal models, with the goal to advance our understanding of how neurons, glia and networks function in vivo.